Search Results
Found 1 results
510(k) Data Aggregation
(57 days)
BriefCase is a radiological computer-aided triage and notification software indicated for use in the analysis of CT exams with contrast (CTA and CT with contrast) that include the chest in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) pathology.
BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of BriefCase are intended to be used in conjunction with other patient information and based on the user's professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
BriefCase is a radiological computer-assisted triage and notification software device. The software system is based on an algorithm programmed component and consists of a standard off-the-shelf operating system, the Microsoft Windows server 2012 64bit, and additional applications, which include PostgreSQL, DICOM module and the BriefCase Image Processing Application. The device consists of the following three modules: (1) Aidoc Hospital Server (AHS/Orchestrator) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Desktop Application for workflow integration.
DICOM images are received, saved, filtered and de-identified before processing. Filtration matches metadata fields with keywords. Series are processed chronologically by running the algorithms on each series to detect suspected cases. The software then flags suspect cases by sending notifications to the desktop application, thereby facilitating triage and prioritization by the user. As the BriefCase software platform harbors several triage algorithms, the user may opt to filter out notifications by pathology, e.g., a chest radiologist may choose to filter out alerts on ICH cases, and a neuro-radiologist would opt to divert AD alerts. Where several medical centers are linked to a shared PACS, a user may read cases for a certain center but not for another, and thus may opt to filter out alerts by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc desktop application.
The desktop application feed displays all incoming suspect cases, each notified case in a line. Hovering over a line in the feed pops up a compressed, low-quality, grayscale, unannotated image that is captioned "not for diagnostic use" and is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification.
Here's a breakdown of the acceptance criteria and study proving the device's performance, based on the provided text:
Device Name: BriefCase
Indication for Use: Radiological computer-aided triage and notification software for analysis of CT exams with contrast (CTA and CT with contrast) including the chest in adults or transitional adolescents (18+) to flag and communicate suspected positive cases of aortic dissection (AD) pathology. Intended to assist in workflow triage/prioritization.
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Performance Goal) | Reported Device Performance (95% CI) |
|---|---|---|
| Sensitivity | ≥ 80% | 93.23% (88.70% - 96.35%) |
| Specificity | ≥ 80% | 92.83% (89.35% - 95.45%) |
Additional Performance Metrics (not explicitly acceptance criteria but reported):
| Metric | Reported Device Performance (95% CI) |
|---|---|
| NPV | 99.8% (99.7% - 99.9%) |
| PPV | 25.0% (18.2% - 19.5%) |
| PLR | 13.010 (8.682 - 19.494) |
| NLR | 0.073 (0.043 - 0.123) |
| Time-to-Notification | 38 seconds (35.5 - 40.4) |
2. Sample Size and Data Provenance
- Test Set Sample Size: 499 cases
- Data Provenance: Retrospective, collected from 5 medical centers in the US. The datasets are explicitly stated to be distinct from the ones used to train the algorithm.
3. Number of Experts and Qualifications for Ground Truth Establishment
The document does not explicitly state the number of experts used or their exact qualifications beyond "reviewers" and describing the process as "identified as positive both by the reviewers as well as the BriefCase device". However, given the context of medical device regulatory submissions, it is implied that these "reviewers" would be appropriately qualified medical professionals, such as radiologists, experienced in diagnosing aortic dissection.
4. Adjudication Method for the Test Set
The document does not explicitly describe a specific adjudication method (e.g., 2+1, 3+1). It states that cases were identified as positive "by the reviewers," implying a consensus or expert determination process for the ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported. The study focused on the standalone performance of the AI algorithm and compared its time-to-notification metric to a predicate device. The document states, "The time-to-notification results obtained for the subject BriefCase device show comparability with the primary predicate with regard to the standard of care review." This implies an efficiency benefit rather than a direct human-AI collaborative performance measurement.
6. Standalone (Algorithm Only) Performance Study
Yes, a standalone performance study was done. The reported sensitivity, specificity, PPV, NPV, PLR, and NLR figures are indicative of the algorithm's performance in identifying aortic dissection without human intervention for the primary assessment. The device operates "in parallel to the ongoing standard of care image interpretation."
7. Type of Ground Truth Used
The ground truth was established by "reviewers." While not explicitly stated to be "expert consensus" or "pathology," in the context of diagnostic imaging, "reviewers" typically refers to expert readers (e.g., radiologists) who establish the presence or absence of the condition based on review of the images and potentially other patient information (similar to "expert consensus").
8. Sample Size for the Training Set
The document does not specify the exact sample size for the training set. It mentions the test data "are distinct datasets from the ones used to train the algorithm."
9. How Ground Truth for Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. It is common practice for such datasets to be expertly annotated, often by radiologists, for pathologies relevant to the algorithm's training.
Ask a specific question about this device
Page 1 of 1